![Risk of sharing data with AI]()
As more and more businesses have started integrating AI into their products and applications, there is a serious concern about send your data to external AI systems such as LLMs.
Let’s learn about various risks and how to mitigate them.
1. Exposing sensitive or proprietary information
By sending a prompt or data to an AI system is giving them control over that data. In other words, one the data is shared, you have no control over it.
Example 1: Imagine you’re building a proprietary software for a healthcare that does cancer diagnosis. You could have your own patented idea and algorithm. By sharing it, you exposes your IP to an third party.
Example 2: Imagine an accounting firm is using OpenAI or other AI company and sending a client’s financial data to the LLM, its literally sharing data with an unknown entity. You have no idea and control over what OpenAI does with this data. To be honest, these big techs are not to be trusted unless there is a legal contract in place.
Example 3: You build a software product and share your internal source code with LLMs. LLM owners such as OpenAI, Google, and others are not friends of anyone. They have already copied everyone’s code from all over the internet.
Example 4: Imagine you’re using OpenAI or ChatGPT that may expose sensitive and private personal data such as social security, date of birth etc. Once this data is exposed, it could go anywhere from there.
Example 5: You ask Copilot to connect with your cloud or bank account and give it your password or the private key to access your account.
2. Data Leakage
Many AI tools, such as ChatGPT and other platforms, use APIs to send data back and forth to backend LLMs. Some of these tools may not be secure and may store your data in their own databases, cached somewhere.
For example, history stored by ChatGPT and Gemini is stored in their databases that their engineers have access to.
3. Loss of Control
Once data is entered in the AI systems, direct or via APIs, you have no control and visibility into how it’s stored, used, or shared—creating compliance and governance challenges.
How to protect your sensitive data when sharing with LLMs
So, how do you mitigate these risks? Here are some thoughts:
Don’t use real production data.
Create seed and test data that is a copy of your production data, but not the real data. For example, if you want an AI platform to create a form that will access your social security number, date of birth, and credit card, you can create a dummy social security number, date of birth, and other data.
Use Test Accounts
If you need to provide your accounts API keys, passwords, and other login information, make sure to create test accounts with test data.
Read Terms and Conditions
Not all LLMs and AI systems store your data. Clearly read their terms and conditions. For example, if Copilot doesn’t share your code with LLM, you can run it locally on your machine and create your own code without sharing it with Microsoft.
Here are other recommended best practices.
Best Practices
- 🔒 Use enterprise agreements with clear data handling terms.
- 🛡️ Mask, anonymize, or tokenize sensitive data before sending.
- 🧠 Prefer private or open-source LLMs for critical workloads and run locally or in your own cloud.
- 📃 Audit and log API interactions for traceability and accountability.
- 🧑🏫 Educate users about safe prompt and input practices.
Need help with data protection?
C# Corner Consulting helps businesses securely integrate LLMs with best practices, custom policies, and enterprise-grade safeguards to protect your data and infrastructure. Contact us here: https://www.c-sharpcorner.com/consulting/